Accurate knowledge of patient and mobile C-arm system orientation is essential for intraoperative 3D scan acquisition, yet this information is currently entered manually by operating room staff, making the process timeconsuming and error-prone.We propose a deep learning approach for the joint classification of patient and C-arm orientation using only a pair of anteriorposterior and lateral projection images. The method builds on frozen DAX foundation model embeddings, combined with a task-specific head network trained on 633 clinical 3D scans. The developed model achieved a weighted mean F1-score of 89.9% (±1.3%). It can be seamlessly incorporated into the clinical 3D workflow to automatically infer orientation from pre-existing scout views, thus reducing the need for manual intervention and enhancing intraoperative efficiency.